store = {}
store['args']={'name': 'emnist_independent_bald_k10_779382', 'available_sample_k': 5, 'num_inference_samples': 10, 'seed': 779382, 'acquisition_method': 'AcquisitionMethod.independent', 'experiment_description': 'EMNIST with b5 and k10, k100 with both BALD and BatchBALD', 'type': 'AcquisitionFunction.bald', 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 16384, 'early_stopping_patience': 3, 'epochs': 40, 'epoch_samples': 20224, 'target_accuracy': 0.85, 'target_num_acquired_samples': 300, 'log_interval': 20, 'dataset': 'DatasetEnum.emnist', 'initial_samples': [], 'experiment_task_id': 20, 'experiments_laaos': './experiment_configs/emnist_bbb/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=20', '--experiments_laaos=./experiment_configs/emnist_bbb/configs.py']
store['iterations']=[]
store['initial_samples']=[]
store['iterations'].append({'num_epochs': 0, 'test_metrics': {'accuracy': 0.02, 'nll': 3.864963630108123}, 'chosen_samples': [87129, 105021, 96828, 73715, 45598], 'chosen_samples_score': [0.01607545390725118, 0.016218922659754664, 0.01619339212775195, 0.01627822406589985, 0.017439154908061028], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.04765957446808511, 'nll': 44.45904713407476}, 'chosen_samples': [55365, 9312, 70027, 95914, 87709], 'chosen_samples_score': [1.1706202217730315, 1.2120275299657912, 1.1722507899085681, 1.1707968898784504, 1.1759718086025273], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.06952127659574468, 'nll': 32.526652197330556}, 'chosen_samples': [79381, 94374, 98942, 94370, 51809], 'chosen_samples_score': [1.2070772624522441, 1.229624952777244, 1.2382737489328592, 1.2894731455701338, 1.2795554901143056], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.075, 'nll': 33.152507600175575}, 'chosen_samples': [51420, 14077, 21225, 52113, 6800], 'chosen_samples_score': [1.582941217701797, 1.5864665186793816, 1.5911144109290676, 1.604878052018651, 1.655139866912665], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.09372340425531915, 'nll': 32.80426158823865}, 'chosen_samples': [19246, 8331, 6784, 27374, 95572], 'chosen_samples_score': [1.513258369127411, 1.5557156905863494, 1.5610807026277513, 1.7053843461507148, 1.5783639156688163], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.09164893617021276, 'nll': 32.179127726453416}, 'chosen_samples': [33514, 18247, 90602, 22084, 47358], 'chosen_samples_score': [1.5845153214290975, 1.6242859156988205, 1.6331326042630883, 1.6134915838614126, 1.6018991364082016], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.11127659574468085, 'nll': 30.898259118262757}, 'chosen_samples': [64102, 83217, 22784, 9437, 78764], 'chosen_samples_score': [1.5690720202959358, 1.7309031613526862, 1.6875727463483698, 1.580912686323307, 1.6054926020727627], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.12840425531914892, 'nll': 28.339057911000353}, 'chosen_samples': [15913, 65723, 92132, 35539, 36261], 'chosen_samples_score': [1.4210930916440545, 1.4222843816023758, 1.423858840117219, 1.4802951378847737, 1.4265761005337239], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.12617021276595744, 'nll': 31.981717418914144}, 'chosen_samples': [5395, 82360, 77073, 33027, 55324], 'chosen_samples_score': [1.6932152807222218, 1.7996708639129084, 1.7073842915596915, 1.7105374509459885, 1.7016338139736074], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.14872340425531916, 'nll': 28.529433228513028}, 'chosen_samples': [39890, 71659, 86144, 74489, 7447], 'chosen_samples_score': [1.7050780109016612, 1.7082292444823006, 1.7641061864504808, 1.7051017153034693, 1.7394042803324274], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.1453191489361702, 'nll': 31.547669294641373}, 'chosen_samples': [1678, 2648, 6202, 110318, 28776], 'chosen_samples_score': [1.7792649719657196, 1.7915236458808628, 1.7932471615243122, 1.8101764295935805, 1.9145606323155184], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.16063829787234044, 'nll': 24.83310080994951}, 'chosen_samples': [59773, 42143, 72345, 53758, 72767], 'chosen_samples_score': [1.5971268342075382, 1.6130706953424352, 1.637854797737641, 1.7053149693561673, 1.6506353290706888], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.16664893617021276, 'nll': 21.005091005690556}, 'chosen_samples': [27856, 8087, 70797, 19882, 68227], 'chosen_samples_score': [1.6824221812090236, 1.6994022332915169, 1.6875371023667647, 1.7351170171262065, 1.7533346483310737], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.1747340425531915, 'nll': 19.633226690089447}, 'chosen_samples': [60607, 57266, 57798, 67425, 111241], 'chosen_samples_score': [1.572301868499686, 1.5821005236785364, 1.591776451846113, 1.6424967963943913, 1.632081504774122], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.19563829787234044, 'nll': 20.88938647168748}, 'chosen_samples': [108541, 13648, 31162, 54811, 47519], 'chosen_samples_score': [1.5804258077181705, 1.5853345427763594, 1.6728576062496079, 1.6685959786425897, 1.5998252672419406], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.19957446808510637, 'nll': 17.3739943751883}, 'chosen_samples': [46554, 40572, 24602, 18573, 17860], 'chosen_samples_score': [1.6954383051275117, 1.7039424222456163, 1.7680149970415906, 1.7118931688403145, 1.7109107589288013], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.211968085106383, 'nll': 18.2976433790491}, 'chosen_samples': [100680, 11838, 108451, 3642, 49362], 'chosen_samples_score': [1.5969025906040415, 1.597477152975335, 1.6358861642292761, 1.603580012214569, 1.669014366770626], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2255851063829787, 'nll': 16.156259953113313}, 'chosen_samples': [7847, 77676, 40243, 13067, 6443], 'chosen_samples_score': [1.6831985739130717, 1.6909053096787772, 1.722468985093021, 1.7575516681163712, 1.8103432543750375], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.22026595744680852, 'nll': 18.6411051145513}, 'chosen_samples': [79882, 57046, 92628, 13306, 106310], 'chosen_samples_score': [1.751399014691674, 1.764030393554238, 1.8493865828032727, 1.8250519893120132, 1.8477792754873654], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2350531914893617, 'nll': 13.87111426170836}, 'chosen_samples': [86164, 81295, 106889, 45111, 100580], 'chosen_samples_score': [1.6201854240604936, 1.622620709374587, 1.6801938921795085, 1.6906879608535963, 1.6419879663880002], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2528191489361702, 'nll': 13.30059658781011}, 'chosen_samples': [76582, 61975, 65873, 76480, 112009], 'chosen_samples_score': [1.5586342938390838, 1.728535158450967, 1.6257887197867458, 1.6880560901386437, 1.6078859796575666], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2650531914893617, 'nll': 13.04432573115572}, 'chosen_samples': [90452, 102858, 56819, 56087, 83979], 'chosen_samples_score': [1.5671020575480514, 1.576536197413187, 1.6250250400404933, 1.5789324032578462, 1.6251425563638404], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.26340425531914896, 'nll': 14.188431425703333}, 'chosen_samples': [101518, 68047, 89392, 78334, 5322], 'chosen_samples_score': [1.7656516975685776, 1.7690672789782613, 1.8414285398517414, 1.9141224337557772, 1.8386801541323028], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2599468085106383, 'nll': 15.022595393404048}, 'chosen_samples': [29476, 41300, 46077, 52282, 96608], 'chosen_samples_score': [1.6970570788497124, 1.728945983814584, 1.7480739614514624, 1.7507043300339744, 1.8005630744202268], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2748404255319149, 'nll': 12.84181643465732}, 'chosen_samples': [77178, 139, 31950, 41840, 26802], 'chosen_samples_score': [1.5802788110346726, 1.581797205403185, 1.6107795198476227, 1.6292281189072493, 1.665839940566741], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2752127659574468, 'nll': 13.503577569190492}, 'chosen_samples': [38083, 105773, 110257, 23775, 109077], 'chosen_samples_score': [1.6540153438935488, 1.657445909853053, 1.6689234631610472, 1.7446300925672058, 1.6902594526903543], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.28308510638297874, 'nll': 11.397298303969363}, 'chosen_samples': [57856, 2690, 47740, 95549, 76815], 'chosen_samples_score': [1.5911511442842177, 1.616885392421952, 1.6518854364222735, 1.6555646098805372, 1.6889115601237599], 'chosen_samples_orignal_score': None})
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